Rank, Chunk and Expand: Lineage-Oriented Reasoning for Taxonomy Expansion
This work addresses taxonomy expansion for recommendation systems and web applications, offering a novel approach to overcome limitations in existing methods.
The paper tackles the problem of expanding taxonomies, which are hierarchical knowledge graphs, by proposing LORex, a framework that combines discriminative ranking and generative reasoning to improve efficiency and accuracy. It achieves a 12% increase in accuracy and a 5% improvement in Wu & Palmer similarity over state-of-the-art methods across four benchmarks.
Taxonomies are hierarchical knowledge graphs crucial for recommendation systems, and web applications. As data grows, expanding taxonomies is essential, but existing methods face key challenges: (1) discriminative models struggle with representation limits and generalization, while (2) generative methods either process all candidates at once, introducing noise and exceeding context limits, or discard relevant entities by selecting noisy candidates. We propose LORex (Lineage-Oriented Reasoning for Taxonomy Expansion), a plug-and-play framework that combines discriminative ranking and generative reasoning for efficient taxonomy expansion. Unlike prior methods, LORex ranks and chunks candidate terms into batches, filtering noise and iteratively refining selections by reasoning candidates' hierarchy to ensure contextual efficiency. Extensive experiments across four benchmarks and twelve baselines show that LORex improves accuracy by 12% and Wu & Palmer similarity by 5% over state-of-the-art methods.